Known for its readability and clarity, this Second Edition of the best-selling Applied Regression provides an accessible introduction to regression analysis for social scientists and other professionals who want to model quantitative data. After covering the basic idea of fitting a straight line to a scatter of data points, the text uses clear language to explain both the mathematics and assumptions behind the simple linear regression model. The authors then cover more specialized subjects of regression analysis, such as multiple regression, measures of model fit, analysis of residuals, interaction effects, multicollinearity, and prediction. Throughout the text, graphical and applied examples help explain and demonstrate the power and broad applicability of regression analysis for answering scientific questions.
This monograph is an excellent introduction to regression analysis. I had the benefit of having the author as a professor almost thirty years ago (does that make me feel kid or does it make him feel old). I still have this on my bookshelf. I still recommend it to those who want a gentle, yet solid understanding of OLS Regression.
A very good introduction to statistics and regression. I've used this in two statistics courses now and it is very easy to understand. Not something I would read on my own, though and I found the subject matter rather boring.
This is a nice brief introduction to regression. Regression is a work horse statistical technique, for instance in the social sciences. This is not written at an advanced level (see Tim Keith's published book on the subject for a more detailed, more technical discussion).